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<div class="title">imageNet.h</div>  </div>
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<a href="imageNet_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> * copy of this software and associated documentation files (the &quot;Software&quot;),</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * to deal in the Software without restriction, including without limitation</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * the rights to use, copy, modify, merge, publish, distribute, sublicense,</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * and/or sell copies of the Software, and to permit persons to whom the</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * Software is furnished to do so, subject to the following conditions:</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> * all copies or substantial portions of the Software.</span></div>
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<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL</span></div>
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<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER</span></div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * DEALINGS IN THE SOFTWARE.</span></div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160; </div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="preprocessor">#ifndef __IMAGE_NET_H__</span></div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#define __IMAGE_NET_H__</span></div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160; </div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160; </div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="tensorNet_8h.html">tensorNet.h</a>&quot;</span></div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160; </div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160; </div>
<div class="line"><a name="l00034"></a><span class="lineno"><a class="line" href="group__imageNet.html#ga00bb3120ef3040793ad3ee25d2727f5b">   34</a></span>&#160;<span class="preprocessor">#define IMAGENET_DEFAULT_INPUT   &quot;data&quot;</span></div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160; </div>
<div class="line"><a name="l00040"></a><span class="lineno"><a class="line" href="group__imageNet.html#ga74a585b96a1bd960b5201f6b69752fad">   40</a></span>&#160;<span class="preprocessor">#define IMAGENET_DEFAULT_OUTPUT  &quot;prob&quot;</span></div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160; </div>
<div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="group__imageNet.html#ga1b2182b95ac7cb13278fad1859de77b7">   46</a></span>&#160;<span class="preprocessor">#define IMAGENET_DEFAULT_THRESHOLD 0.01f</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160; </div>
<div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="group__imageNet.html#ga25d8a439352ed0c7c8b2f1d5488af998">   52</a></span>&#160;<span class="preprocessor">#define IMAGENET_MODEL_TYPE &quot;classification&quot;</span></div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160; </div>
<div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="group__imageNet.html#gab0d359b9760ffe34b09adbb31d8fed54">   58</a></span>&#160;<span class="preprocessor">#define IMAGENET_USAGE_STRING  &quot;imageNet arguments: \n&quot;                                                         \</span></div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="preprocessor">                  &quot;  --network=NETWORK    pre-trained model to load, one of the following:\n&quot;   \</span></div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="preprocessor">                  &quot;                           * alexnet\n&quot;                                                              \</span></div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="preprocessor">                  &quot;                           * googlenet (default)\n&quot;                                  \</span></div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="preprocessor">                  &quot;                           * googlenet-12\n&quot;                                                         \</span></div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;<span class="preprocessor">                  &quot;                           * resnet-18\n&quot;                                                    \</span></div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;<span class="preprocessor">                  &quot;                           * resnet-50\n&quot;                                                    \</span></div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;<span class="preprocessor">                  &quot;                           * resnet-101\n&quot;                                                   \</span></div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;<span class="preprocessor">                  &quot;                           * resnet-152\n&quot;                                                   \</span></div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;<span class="preprocessor">                  &quot;                           * vgg-16\n&quot;                                                               \</span></div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;<span class="preprocessor">                  &quot;                           * vgg-19\n&quot;                                                               \</span></div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="preprocessor">                  &quot;                           * inception-v4\n&quot;                                                         \</span></div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;<span class="preprocessor">                  &quot;  --model=MODEL        path to custom model to load (caffemodel, uff, or onnx)\n&quot;                    \</span></div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;<span class="preprocessor">                  &quot;  --prototxt=PROTOTXT  path to custom prototxt to load (for .caffemodel only)\n&quot;                             \</span></div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;<span class="preprocessor">                  &quot;  --labels=LABELS      path to text file containing the labels for each class\n&quot;                             \</span></div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;<span class="preprocessor">                  &quot;  --input-blob=INPUT   name of the input layer (default is &#39;&quot; IMAGENET_DEFAULT_INPUT &quot;&#39;)\n&quot;  \</span></div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;<span class="preprocessor">                  &quot;  --output-blob=OUTPUT name of the output layer (default is &#39;&quot; IMAGENET_DEFAULT_OUTPUT &quot;&#39;)\n&quot;        \</span></div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;<span class="preprocessor">                  &quot;  --threshold=CONF     minimum confidence threshold for classification (default is 0.01)\n&quot;  \</span></div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;<span class="preprocessor">                  &quot;  --smoothing=WEIGHT   weight between [0,1] or number of frames (disabled by default)\n&quot;             \</span></div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;<span class="preprocessor">                  &quot;  --profile            enable layer profiling in TensorRT\n\n&quot;</span></div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160; </div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160; </div>
<div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="group__imageNet.html">   84</a></span>&#160;<span class="keyword">class </span><a class="code" href="group__imageNet.html#classimageNet">imageNet</a> : <span class="keyword">public</span> <a class="code" href="group__tensorNet.html#classtensorNet">tensorNet</a></div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;{</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;<span class="keyword">public</span>:</div>
<div class="line"><a name="l00090"></a><span class="lineno"><a class="line" href="group__imageNet.html#aae203e533ecceb314857f99a2817fc81">   90</a></span>&#160;        <span class="keyword">typedef</span> std::vector&lt;std::pair&lt;uint32_t, float&gt;&gt; <a class="code" href="group__imageNet.html#aae203e533ecceb314857f99a2817fc81">Classifications</a>;</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        </div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        <span class="keyword">static</span> <a class="code" href="group__imageNet.html#classimageNet">imageNet</a>* <a class="code" href="group__imageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767">Create</a>( <span class="keyword">const</span> <span class="keywordtype">char</span>* network=<span class="stringliteral">&quot;googlenet&quot;</span>, </div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;                                                uint32_t maxBatchSize=<a class="code" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, </div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;                                                <a class="code" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="code" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>,</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;                                                <a class="code" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="code" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, <span class="keywordtype">bool</span> allowGPUFallback=<span class="keyword">true</span> );</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;                                                </div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;        <span class="keyword">static</span> <a class="code" href="group__imageNet.html#classimageNet">imageNet</a>* <a class="code" href="group__imageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767">Create</a>( <span class="keyword">const</span> <span class="keywordtype">char</span>* prototxt_path, <span class="keyword">const</span> <span class="keywordtype">char</span>* model_path, </div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;                                                <span class="keyword">const</span> <span class="keywordtype">char</span>* mean_binary, <span class="keyword">const</span> <span class="keywordtype">char</span>* class_labels, </div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;                                                <span class="keyword">const</span> <span class="keywordtype">char</span>* input=<a class="code" href="group__imageNet.html#ga00bb3120ef3040793ad3ee25d2727f5b">IMAGENET_DEFAULT_INPUT</a>, </div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;                                                <span class="keyword">const</span> <span class="keywordtype">char</span>* output=<a class="code" href="group__imageNet.html#ga74a585b96a1bd960b5201f6b69752fad">IMAGENET_DEFAULT_OUTPUT</a>, </div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;                                                uint32_t maxBatchSize=<a class="code" href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a>, </div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;                                                <a class="code" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision=<a class="code" href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a>,</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;                                                <a class="code" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device=<a class="code" href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a>, <span class="keywordtype">bool</span> allowGPUFallback=<span class="keyword">true</span> );</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        </div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;        <span class="keyword">static</span> <a class="code" href="group__imageNet.html#classimageNet">imageNet</a>* <a class="code" href="group__imageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767">Create</a>( <span class="keywordtype">int</span> argc, <span class="keywordtype">char</span>** argv );</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160; </div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        <span class="keyword">static</span> <a class="code" href="group__imageNet.html#classimageNet">imageNet</a>* <a class="code" href="group__imageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767">Create</a>( <span class="keyword">const</span> <a class="code" href="group__commandLine.html#classcommandLine">commandLine</a>&amp; cmdLine );</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160; </div>
<div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="group__imageNet.html#a7629a888728ef94bf35e573a96ebe4bd">  138</a></span>&#160;        <span class="keyword">static</span> <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="group__imageNet.html#a7629a888728ef94bf35e573a96ebe4bd">Usage</a>()               { <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#gab0d359b9760ffe34b09adbb31d8fed54">IMAGENET_USAGE_STRING</a>; }</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160; </div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;        <span class="keyword">virtual</span> <a class="code" href="group__imageNet.html#af6bd86e81ff9e67ffe19b575c17ed104">~imageNet</a>();</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;                        </div>
<div class="line"><a name="l00158"></a><span class="lineno"><a class="line" href="group__imageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">  158</a></span>&#160;        <span class="keyword">template</span>&lt;<span class="keyword">typename</span> T&gt; <span class="keywordtype">int</span> <a class="code" href="group__imageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">Classify</a>( T* image, uint32_t width, uint32_t height, <span class="keywordtype">float</span>* confidence=NULL )          { <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">Classify</a>((<span class="keywordtype">void</span>*)image, width, height, imageFormatFromType&lt;T&gt;(), confidence); }</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        </div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;        <span class="keywordtype">int</span> <a class="code" href="group__imageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">Classify</a>( <span class="keywordtype">void</span>* image, uint32_t width, uint32_t height, <a class="code" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format, <span class="keywordtype">float</span>* confidence=NULL );</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160; </div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        <span class="keywordtype">int</span> <a class="code" href="group__imageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">Classify</a>( <span class="keywordtype">float</span>* rgba, uint32_t width, uint32_t height, <span class="keywordtype">float</span>* confidence=NULL, <a class="code" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format=<a class="code" href="group__imageFormat.html#gga931c48e08f361637d093355d64583406a9396c3fdae6987bbf4abc2b2e63e3815">IMAGE_RGBA32F</a> );</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160; </div>
<div class="line"><a name="l00208"></a><span class="lineno"><a class="line" href="group__imageNet.html#ad39000debfd9ac6352f85b93ae27b305">  208</a></span>&#160;        <span class="keyword">template</span>&lt;<span class="keyword">typename</span> T&gt; <span class="keywordtype">int</span> <a class="code" href="group__imageNet.html#ad39000debfd9ac6352f85b93ae27b305">Classify</a>( T* image, uint32_t width, uint32_t height, <a class="code" href="group__imageNet.html#aae203e533ecceb314857f99a2817fc81">Classifications</a>&amp; classifications, <span class="keywordtype">int</span> topK=1 )            { <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#ad39000debfd9ac6352f85b93ae27b305">Classify</a>((<span class="keywordtype">void</span>*)image, width, height, imageFormatFromType&lt;T&gt;(), classifications, topK); }</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160; </div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;        <span class="keywordtype">int</span> <a class="code" href="group__imageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">Classify</a>( <span class="keywordtype">void</span>* image, uint32_t width, uint32_t height, <a class="code" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format, <a class="code" href="group__imageNet.html#aae203e533ecceb314857f99a2817fc81">Classifications</a>&amp; classifications, <span class="keywordtype">int</span> topK=1 );</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160; </div>
<div class="line"><a name="l00231"></a><span class="lineno"><a class="line" href="group__imageNet.html#a478f25126524a256e81ec264aad7e27a">  231</a></span>&#160;        <span class="keyword">inline</span> uint32_t <a class="code" href="group__imageNet.html#a478f25126524a256e81ec264aad7e27a">GetNumClasses</a>()<span class="keyword"> const                                           </span>{ <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#a2ba83995003fe4c10d43d52dcb77dd02">mNumClasses</a>; }</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        </div>
<div class="line"><a name="l00236"></a><span class="lineno"><a class="line" href="group__imageNet.html#a3b41ed0e039638353e6964ada588becb">  236</a></span>&#160;        <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="group__imageNet.html#a3b41ed0e039638353e6964ada588becb">GetClassLabel</a>( <span class="keywordtype">int</span> index )<span class="keyword"> const                     </span>{ <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#a673728c04ae909cb3068c2a1ace1e5a7">GetClassDesc</a>(index); }</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;        </div>
<div class="line"><a name="l00241"></a><span class="lineno"><a class="line" href="group__imageNet.html#a673728c04ae909cb3068c2a1ace1e5a7">  241</a></span>&#160;        <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="group__imageNet.html#a673728c04ae909cb3068c2a1ace1e5a7">GetClassDesc</a>( <span class="keywordtype">int</span> index )<span class="keyword">    const                   </span>{ <span class="keywordflow">return</span> index &gt;= 0 ? <a class="code" href="group__imageNet.html#a9c75cea83d0c3e605aef8c0dd8e43177">mClassDesc</a>[index].c_str() : <span class="stringliteral">&quot;none&quot;</span>; }</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        </div>
<div class="line"><a name="l00246"></a><span class="lineno"><a class="line" href="group__imageNet.html#ab8ee0abaa0ebf38becfc0cbaf6956712">  246</a></span>&#160;        <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="group__imageNet.html#ab8ee0abaa0ebf38becfc0cbaf6956712">GetClassSynset</a>( <span class="keywordtype">int</span> index )<span class="keyword"> const                    </span>{ <span class="keywordflow">return</span> index &gt;= 0 ? <a class="code" href="group__imageNet.html#abd00b812a1f39a0bd23c43a8807d6193">mClassSynset</a>[index].c_str() : <span class="stringliteral">&quot;none&quot;</span>; }</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        </div>
<div class="line"><a name="l00251"></a><span class="lineno"><a class="line" href="group__imageNet.html#a04276f915b0f40d6257cbed3fe47dc5f">  251</a></span>&#160;        <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="group__imageNet.html#a04276f915b0f40d6257cbed3fe47dc5f">GetClassPath</a>()<span class="keyword"> const                                         </span>{ <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#a7bce88c4d67550b5d059a4b9cdbb90c1">mClassPath</a>.c_str(); }</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        </div>
<div class="line"><a name="l00256"></a><span class="lineno"><a class="line" href="group__imageNet.html#ac074f4e67f639c89238a3dcb6771c4cd">  256</a></span>&#160;        <span class="keyword">inline</span> <span class="keywordtype">float</span> <a class="code" href="group__imageNet.html#ac074f4e67f639c89238a3dcb6771c4cd">GetThreshold</a>()<span class="keyword"> const                                                       </span>{ <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#a1db7d7ac6160c242b5388139d1bd8030">mThreshold</a>; }</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        </div>
<div class="line"><a name="l00263"></a><span class="lineno"><a class="line" href="group__imageNet.html#a6594a1190c5d515c1987aefcee5d819f">  263</a></span>&#160;        <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="group__imageNet.html#a6594a1190c5d515c1987aefcee5d819f">SetThreshold</a>( <span class="keywordtype">float</span> threshold )                                     { <a class="code" href="group__imageNet.html#a1db7d7ac6160c242b5388139d1bd8030">mThreshold</a> = threshold; }</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        </div>
<div class="line"><a name="l00269"></a><span class="lineno"><a class="line" href="group__imageNet.html#a37b850912da4c60f93c7798d13baf777">  269</a></span>&#160;        <span class="keyword">inline</span> <span class="keywordtype">float</span> <a class="code" href="group__imageNet.html#a37b850912da4c60f93c7798d13baf777">GetSmoothing</a>()<span class="keyword"> const                                                       </span>{ <span class="keywordflow">return</span> <a class="code" href="group__imageNet.html#ac41444447cc6a5caa2430af2a6633392">mSmoothingFactor</a>; }</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;        </div>
<div class="line"><a name="l00289"></a><span class="lineno"><a class="line" href="group__imageNet.html#adc93ae6107469e3898872917f6736d85">  289</a></span>&#160;        <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="group__imageNet.html#adc93ae6107469e3898872917f6736d85">SetSmoothing</a>( <span class="keywordtype">float</span> factor )                                        { <a class="code" href="group__imageNet.html#ac41444447cc6a5caa2430af2a6633392">mSmoothingFactor</a> = factor; }</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160; </div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;<span class="keyword">protected</span>:</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        <a class="code" href="group__imageNet.html#a0ea17be1ce78b3e0758af46c970a968c">imageNet</a>();</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;        </div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        <span class="comment">//bool init( NetworkType networkType, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback );</span></div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;        <span class="keywordtype">bool</span> <a class="code" href="group__imageNet.html#aa5321e8082e2dc35f5982882fa284181">init</a>(<span class="keyword">const</span> <span class="keywordtype">char</span>* prototxt_path, <span class="keyword">const</span> <span class="keywordtype">char</span>* model_path, <span class="keyword">const</span> <span class="keywordtype">char</span>* mean_binary, <span class="keyword">const</span> <span class="keywordtype">char</span>* class_path, <span class="keyword">const</span> <span class="keywordtype">char</span>* input, <span class="keyword">const</span> <span class="keywordtype">char</span>* output, uint32_t maxBatchSize, <a class="code" href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a> precision, <a class="code" href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a> device, <span class="keywordtype">bool</span> allowGPUFallback );</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;        <span class="keywordtype">bool</span> <a class="code" href="group__imageNet.html#a6beef2c8d0972eaadad37abc89e74f95">loadClassInfo</a>( <span class="keyword">const</span> <span class="keywordtype">char</span>* filename, <span class="keywordtype">int</span> expectedClasses=-1 );</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;        </div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;        <span class="keywordtype">bool</span> <a class="code" href="group__imageNet.html#abb118d7cf3f394a4e2d934c2c100fd1a">preProcess</a>( <span class="keywordtype">void</span>* image, uint32_t width, uint32_t height, <a class="code" href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a> format );</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;        </div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;        <span class="keywordtype">float</span>* <a class="code" href="group__imageNet.html#a7e37e2320f4e40263a8169a4ee8d3280">applySmoothing</a>();</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;        </div>
<div class="line"><a name="l00302"></a><span class="lineno"><a class="line" href="group__imageNet.html#a2ba83995003fe4c10d43d52dcb77dd02">  302</a></span>&#160;        uint32_t <a class="code" href="group__imageNet.html#a2ba83995003fe4c10d43d52dcb77dd02">mNumClasses</a>;</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;        </div>
<div class="line"><a name="l00304"></a><span class="lineno"><a class="line" href="group__imageNet.html#abd00b812a1f39a0bd23c43a8807d6193">  304</a></span>&#160;        std::vector&lt;std::string&gt; <a class="code" href="group__imageNet.html#abd00b812a1f39a0bd23c43a8807d6193">mClassSynset</a>;  <span class="comment">// 1000 class ID&#39;s (ie n01580077, n04325704)</span></div>
<div class="line"><a name="l00305"></a><span class="lineno"><a class="line" href="group__imageNet.html#a9c75cea83d0c3e605aef8c0dd8e43177">  305</a></span>&#160;        std::vector&lt;std::string&gt; <a class="code" href="group__imageNet.html#a9c75cea83d0c3e605aef8c0dd8e43177">mClassDesc</a>;</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160; </div>
<div class="line"><a name="l00307"></a><span class="lineno"><a class="line" href="group__imageNet.html#a7bce88c4d67550b5d059a4b9cdbb90c1">  307</a></span>&#160;        std::string <a class="code" href="group__imageNet.html#a7bce88c4d67550b5d059a4b9cdbb90c1">mClassPath</a>;</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;        <span class="comment">//NetworkType mNetworkType;</span></div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;        </div>
<div class="line"><a name="l00310"></a><span class="lineno"><a class="line" href="group__imageNet.html#a1830387e82c00c7d02cf8e884e16c164">  310</a></span>&#160;        <span class="keywordtype">float</span>* <a class="code" href="group__imageNet.html#a1830387e82c00c7d02cf8e884e16c164">mSmoothingBuffer</a>;</div>
<div class="line"><a name="l00311"></a><span class="lineno"><a class="line" href="group__imageNet.html#ac41444447cc6a5caa2430af2a6633392">  311</a></span>&#160;        <span class="keywordtype">float</span>  <a class="code" href="group__imageNet.html#ac41444447cc6a5caa2430af2a6633392">mSmoothingFactor</a>;</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;        </div>
<div class="line"><a name="l00313"></a><span class="lineno"><a class="line" href="group__imageNet.html#a1db7d7ac6160c242b5388139d1bd8030">  313</a></span>&#160;        <span class="keywordtype">float</span> <a class="code" href="group__imageNet.html#a1db7d7ac6160c242b5388139d1bd8030">mThreshold</a>;</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;};</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160; </div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160; </div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;<span class="preprocessor">#endif</span></div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<div class="ttc" id="agroup__imageNet_html_a7e37e2320f4e40263a8169a4ee8d3280"><div class="ttname"><a href="group__imageNet.html#a7e37e2320f4e40263a8169a4ee8d3280">imageNet::applySmoothing</a></div><div class="ttdeci">float * applySmoothing()</div></div>
<div class="ttc" id="agroup__imageFormat_html_gga931c48e08f361637d093355d64583406a9396c3fdae6987bbf4abc2b2e63e3815"><div class="ttname"><a href="group__imageFormat.html#gga931c48e08f361637d093355d64583406a9396c3fdae6987bbf4abc2b2e63e3815">IMAGE_RGBA32F</a></div><div class="ttdeci">@ IMAGE_RGBA32F</div><div class="ttdoc">float4 RGBA32F (‘'rgba32f’`)</div><div class="ttdef"><b>Definition:</b> imageFormat.h:55</div></div>
<div class="ttc" id="agroup__imageNet_html_a0023fb5bcd45dcefdac6a7df4bf9bab5"><div class="ttname"><a href="group__imageNet.html#a0023fb5bcd45dcefdac6a7df4bf9bab5">imageNet::Classify</a></div><div class="ttdeci">int Classify(T *image, uint32_t width, uint32_t height, float *confidence=NULL)</div><div class="ttdoc">Predict the maximum-likelihood image class whose confidence meets the minimum threshold.</div><div class="ttdef"><b>Definition:</b> imageNet.h:158</div></div>
<div class="ttc" id="agroup__imageNet_html_a7629a888728ef94bf35e573a96ebe4bd"><div class="ttname"><a href="group__imageNet.html#a7629a888728ef94bf35e573a96ebe4bd">imageNet::Usage</a></div><div class="ttdeci">static const char * Usage()</div><div class="ttdoc">Usage string for command line arguments to Create()</div><div class="ttdef"><b>Definition:</b> imageNet.h:138</div></div>
<div class="ttc" id="agroup__imageNet_html_ga00bb3120ef3040793ad3ee25d2727f5b"><div class="ttname"><a href="group__imageNet.html#ga00bb3120ef3040793ad3ee25d2727f5b">IMAGENET_DEFAULT_INPUT</a></div><div class="ttdeci">#define IMAGENET_DEFAULT_INPUT</div><div class="ttdoc">Name of default input blob for imageNet model.</div><div class="ttdef"><b>Definition:</b> imageNet.h:34</div></div>
<div class="ttc" id="agroup__imageNet_html_ad39000debfd9ac6352f85b93ae27b305"><div class="ttname"><a href="group__imageNet.html#ad39000debfd9ac6352f85b93ae27b305">imageNet::Classify</a></div><div class="ttdeci">int Classify(T *image, uint32_t width, uint32_t height, Classifications &amp;classifications, int topK=1)</div><div class="ttdoc">Classify the image and return the topK image classification results that meet the minimum confidence ...</div><div class="ttdef"><b>Definition:</b> imageNet.h:208</div></div>
<div class="ttc" id="agroup__imageNet_html_a37b850912da4c60f93c7798d13baf777"><div class="ttname"><a href="group__imageNet.html#a37b850912da4c60f93c7798d13baf777">imageNet::GetSmoothing</a></div><div class="ttdeci">float GetSmoothing() const</div><div class="ttdoc">Return the temporal smoothing weight or number of frames in the smoothing window.</div><div class="ttdef"><b>Definition:</b> imageNet.h:269</div></div>
<div class="ttc" id="agroup__imageNet_html_ab8ee0abaa0ebf38becfc0cbaf6956712"><div class="ttname"><a href="group__imageNet.html#ab8ee0abaa0ebf38becfc0cbaf6956712">imageNet::GetClassSynset</a></div><div class="ttdeci">const char * GetClassSynset(int index) const</div><div class="ttdoc">Retrieve the class synset category of a particular class.</div><div class="ttdef"><b>Definition:</b> imageNet.h:246</div></div>
<div class="ttc" id="agroup__imageNet_html_ga74a585b96a1bd960b5201f6b69752fad"><div class="ttname"><a href="group__imageNet.html#ga74a585b96a1bd960b5201f6b69752fad">IMAGENET_DEFAULT_OUTPUT</a></div><div class="ttdeci">#define IMAGENET_DEFAULT_OUTPUT</div><div class="ttdoc">Name of default output confidence values for imageNet model.</div><div class="ttdef"><b>Definition:</b> imageNet.h:40</div></div>
<div class="ttc" id="agroup__imageNet_html_a1830387e82c00c7d02cf8e884e16c164"><div class="ttname"><a href="group__imageNet.html#a1830387e82c00c7d02cf8e884e16c164">imageNet::mSmoothingBuffer</a></div><div class="ttdeci">float * mSmoothingBuffer</div><div class="ttdef"><b>Definition:</b> imageNet.h:310</div></div>
<div class="ttc" id="agroup__imageNet_html_adc93ae6107469e3898872917f6736d85"><div class="ttname"><a href="group__imageNet.html#adc93ae6107469e3898872917f6736d85">imageNet::SetSmoothing</a></div><div class="ttdeci">void SetSmoothing(float factor)</div><div class="ttdoc">Enable temporal smoothing of the results using EWMA (exponentially-weighted moving average).</div><div class="ttdef"><b>Definition:</b> imageNet.h:289</div></div>
<div class="ttc" id="agroup__tensorNet_html_ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b"><div class="ttname"><a href="group__tensorNet.html#ggaa5d3f9981cdbd91516c1474006a80fe4adc7f3f88455afa81458863e5b3092e4b">DEVICE_GPU</a></div><div class="ttdeci">@ DEVICE_GPU</div><div class="ttdoc">GPU (if multiple GPUs are present, a specific GPU can be selected with cudaSetDevice()</div><div class="ttdef"><b>Definition:</b> tensorNet.h:131</div></div>
<div class="ttc" id="agroup__imageNet_html_a3b41ed0e039638353e6964ada588becb"><div class="ttname"><a href="group__imageNet.html#a3b41ed0e039638353e6964ada588becb">imageNet::GetClassLabel</a></div><div class="ttdeci">const char * GetClassLabel(int index) const</div><div class="ttdoc">Retrieve the description of a particular class.</div><div class="ttdef"><b>Definition:</b> imageNet.h:236</div></div>
<div class="ttc" id="agroup__tensorNet_html_gaa5d3f9981cdbd91516c1474006a80fe4"><div class="ttname"><a href="group__tensorNet.html#gaa5d3f9981cdbd91516c1474006a80fe4">deviceType</a></div><div class="ttdeci">deviceType</div><div class="ttdoc">Enumeration for indicating the desired device that the network should run on, if available in hardwar...</div><div class="ttdef"><b>Definition:</b> tensorNet.h:129</div></div>
<div class="ttc" id="agroup__imageNet_html_a478f25126524a256e81ec264aad7e27a"><div class="ttname"><a href="group__imageNet.html#a478f25126524a256e81ec264aad7e27a">imageNet::GetNumClasses</a></div><div class="ttdeci">uint32_t GetNumClasses() const</div><div class="ttdoc">Retrieve the number of image recognition classes (typically 1000)</div><div class="ttdef"><b>Definition:</b> imageNet.h:231</div></div>
<div class="ttc" id="atensorNet_8h_html"><div class="ttname"><a href="tensorNet_8h.html">tensorNet.h</a></div></div>
<div class="ttc" id="agroup__imageNet_html_abd00b812a1f39a0bd23c43a8807d6193"><div class="ttname"><a href="group__imageNet.html#abd00b812a1f39a0bd23c43a8807d6193">imageNet::mClassSynset</a></div><div class="ttdeci">std::vector&lt; std::string &gt; mClassSynset</div><div class="ttdef"><b>Definition:</b> imageNet.h:304</div></div>
<div class="ttc" id="agroup__tensorNet_html_ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9"><div class="ttname"><a href="group__tensorNet.html#ggaac6604fd52c6e5db82877390e0378623a1d325738f49e8e4c424ff671624e66f9">TYPE_FASTEST</a></div><div class="ttdeci">@ TYPE_FASTEST</div><div class="ttdoc">The fastest detected precision should be use (i.e.</div><div class="ttdef"><b>Definition:</b> tensorNet.h:105</div></div>
<div class="ttc" id="agroup__imageNet_html_aae203e533ecceb314857f99a2817fc81"><div class="ttname"><a href="group__imageNet.html#aae203e533ecceb314857f99a2817fc81">imageNet::Classifications</a></div><div class="ttdeci">std::vector&lt; std::pair&lt; uint32_t, float &gt; &gt; Classifications</div><div class="ttdoc">List of classification results where each entry represents a (classID, confidence) pair.</div><div class="ttdef"><b>Definition:</b> imageNet.h:90</div></div>
<div class="ttc" id="agroup__imageNet_html_ac074f4e67f639c89238a3dcb6771c4cd"><div class="ttname"><a href="group__imageNet.html#ac074f4e67f639c89238a3dcb6771c4cd">imageNet::GetThreshold</a></div><div class="ttdeci">float GetThreshold() const</div><div class="ttdoc">Return the confidence threshold used for classification.</div><div class="ttdef"><b>Definition:</b> imageNet.h:256</div></div>
<div class="ttc" id="agroup__tensorNet_html_gaac6604fd52c6e5db82877390e0378623"><div class="ttname"><a href="group__tensorNet.html#gaac6604fd52c6e5db82877390e0378623">precisionType</a></div><div class="ttdeci">precisionType</div><div class="ttdoc">Enumeration for indicating the desired precision that the network should run in, if available in hard...</div><div class="ttdef"><b>Definition:</b> tensorNet.h:102</div></div>
<div class="ttc" id="agroup__imageNet_html_abb118d7cf3f394a4e2d934c2c100fd1a"><div class="ttname"><a href="group__imageNet.html#abb118d7cf3f394a4e2d934c2c100fd1a">imageNet::preProcess</a></div><div class="ttdeci">bool preProcess(void *image, uint32_t width, uint32_t height, imageFormat format)</div></div>
<div class="ttc" id="agroup__imageNet_html_af6bd86e81ff9e67ffe19b575c17ed104"><div class="ttname"><a href="group__imageNet.html#af6bd86e81ff9e67ffe19b575c17ed104">imageNet::~imageNet</a></div><div class="ttdeci">virtual ~imageNet()</div><div class="ttdoc">Destroy.</div></div>
<div class="ttc" id="agroup__imageNet_html_a9c75cea83d0c3e605aef8c0dd8e43177"><div class="ttname"><a href="group__imageNet.html#a9c75cea83d0c3e605aef8c0dd8e43177">imageNet::mClassDesc</a></div><div class="ttdeci">std::vector&lt; std::string &gt; mClassDesc</div><div class="ttdef"><b>Definition:</b> imageNet.h:305</div></div>
<div class="ttc" id="agroup__tensorNet_html_classtensorNet"><div class="ttname"><a href="group__tensorNet.html#classtensorNet">tensorNet</a></div><div class="ttdoc">Abstract class for loading a tensor network with TensorRT.</div><div class="ttdef"><b>Definition:</b> tensorNet.h:218</div></div>
<div class="ttc" id="agroup__imageNet_html_a04276f915b0f40d6257cbed3fe47dc5f"><div class="ttname"><a href="group__imageNet.html#a04276f915b0f40d6257cbed3fe47dc5f">imageNet::GetClassPath</a></div><div class="ttdeci">const char * GetClassPath() const</div><div class="ttdoc">Retrieve the path to the file containing the class descriptions.</div><div class="ttdef"><b>Definition:</b> imageNet.h:251</div></div>
<div class="ttc" id="agroup__imageNet_html_classimageNet"><div class="ttname"><a href="group__imageNet.html#classimageNet">imageNet</a></div><div class="ttdoc">Image recognition with classification networks, using TensorRT.</div><div class="ttdef"><b>Definition:</b> imageNet.h:84</div></div>
<div class="ttc" id="agroup__imageNet_html_a1db7d7ac6160c242b5388139d1bd8030"><div class="ttname"><a href="group__imageNet.html#a1db7d7ac6160c242b5388139d1bd8030">imageNet::mThreshold</a></div><div class="ttdeci">float mThreshold</div><div class="ttdef"><b>Definition:</b> imageNet.h:313</div></div>
<div class="ttc" id="agroup__imageNet_html_a7bce88c4d67550b5d059a4b9cdbb90c1"><div class="ttname"><a href="group__imageNet.html#a7bce88c4d67550b5d059a4b9cdbb90c1">imageNet::mClassPath</a></div><div class="ttdeci">std::string mClassPath</div><div class="ttdef"><b>Definition:</b> imageNet.h:307</div></div>
<div class="ttc" id="agroup__imageNet_html_a2ba83995003fe4c10d43d52dcb77dd02"><div class="ttname"><a href="group__imageNet.html#a2ba83995003fe4c10d43d52dcb77dd02">imageNet::mNumClasses</a></div><div class="ttdeci">uint32_t mNumClasses</div><div class="ttdef"><b>Definition:</b> imageNet.h:302</div></div>
<div class="ttc" id="agroup__imageNet_html_gab0d359b9760ffe34b09adbb31d8fed54"><div class="ttname"><a href="group__imageNet.html#gab0d359b9760ffe34b09adbb31d8fed54">IMAGENET_USAGE_STRING</a></div><div class="ttdeci">#define IMAGENET_USAGE_STRING</div><div class="ttdoc">Standard command-line options able to be passed to imageNet::Create()</div><div class="ttdef"><b>Definition:</b> imageNet.h:58</div></div>
<div class="ttc" id="agroup__imageNet_html_a0ea17be1ce78b3e0758af46c970a968c"><div class="ttname"><a href="group__imageNet.html#a0ea17be1ce78b3e0758af46c970a968c">imageNet::imageNet</a></div><div class="ttdeci">imageNet()</div></div>
<div class="ttc" id="agroup__imageNet_html_a673728c04ae909cb3068c2a1ace1e5a7"><div class="ttname"><a href="group__imageNet.html#a673728c04ae909cb3068c2a1ace1e5a7">imageNet::GetClassDesc</a></div><div class="ttdeci">const char * GetClassDesc(int index) const</div><div class="ttdoc">Retrieve the description of a particular class.</div><div class="ttdef"><b>Definition:</b> imageNet.h:241</div></div>
<div class="ttc" id="agroup__tensorNet_html_ga5a46a965749d6118e01307fd4d4865c9"><div class="ttname"><a href="group__tensorNet.html#ga5a46a965749d6118e01307fd4d4865c9">DEFAULT_MAX_BATCH_SIZE</a></div><div class="ttdeci">#define DEFAULT_MAX_BATCH_SIZE</div><div class="ttdoc">Default maximum batch size.</div><div class="ttdef"><b>Definition:</b> tensorNet.h:88</div></div>
<div class="ttc" id="agroup__imageNet_html_ac41444447cc6a5caa2430af2a6633392"><div class="ttname"><a href="group__imageNet.html#ac41444447cc6a5caa2430af2a6633392">imageNet::mSmoothingFactor</a></div><div class="ttdeci">float mSmoothingFactor</div><div class="ttdef"><b>Definition:</b> imageNet.h:311</div></div>
<div class="ttc" id="agroup__imageNet_html_aa5321e8082e2dc35f5982882fa284181"><div class="ttname"><a href="group__imageNet.html#aa5321e8082e2dc35f5982882fa284181">imageNet::init</a></div><div class="ttdeci">bool init(const char *prototxt_path, const char *model_path, const char *mean_binary, const char *class_path, const char *input, const char *output, uint32_t maxBatchSize, precisionType precision, deviceType device, bool allowGPUFallback)</div></div>
<div class="ttc" id="agroup__imageNet_html_a6fddcf6fa38d337dbf0c9c8d64fca767"><div class="ttname"><a href="group__imageNet.html#a6fddcf6fa38d337dbf0c9c8d64fca767">imageNet::Create</a></div><div class="ttdeci">static imageNet * Create(const char *network=&quot;googlenet&quot;, uint32_t maxBatchSize=DEFAULT_MAX_BATCH_SIZE, precisionType precision=TYPE_FASTEST, deviceType device=DEVICE_GPU, bool allowGPUFallback=true)</div><div class="ttdoc">Load one of the following pre-trained models:</div></div>
<div class="ttc" id="agroup__imageNet_html_a6beef2c8d0972eaadad37abc89e74f95"><div class="ttname"><a href="group__imageNet.html#a6beef2c8d0972eaadad37abc89e74f95">imageNet::loadClassInfo</a></div><div class="ttdeci">bool loadClassInfo(const char *filename, int expectedClasses=-1)</div></div>
<div class="ttc" id="agroup__commandLine_html_classcommandLine"><div class="ttname"><a href="group__commandLine.html#classcommandLine">commandLine</a></div><div class="ttdoc">Command line parser for extracting flags, values, and strings.</div><div class="ttdef"><b>Definition:</b> commandLine.h:35</div></div>
<div class="ttc" id="agroup__imageFormat_html_ga931c48e08f361637d093355d64583406"><div class="ttname"><a href="group__imageFormat.html#ga931c48e08f361637d093355d64583406">imageFormat</a></div><div class="ttdeci">imageFormat</div><div class="ttdoc">The imageFormat enum is used to identify the pixel format and colorspace of an image.</div><div class="ttdef"><b>Definition:</b> imageFormat.h:49</div></div>
<div class="ttc" id="agroup__imageNet_html_a6594a1190c5d515c1987aefcee5d819f"><div class="ttname"><a href="group__imageNet.html#a6594a1190c5d515c1987aefcee5d819f">imageNet::SetThreshold</a></div><div class="ttdeci">void SetThreshold(float threshold)</div><div class="ttdoc">Set the confidence threshold used for classification.</div><div class="ttdef"><b>Definition:</b> imageNet.h:263</div></div>
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